def plot_tree(ax, tree, plane='xy',
diameter_scale=_DIAMETER_SCALE, linewidth=_LINEWIDTH,
color=None, alpha=_ALPHA, realistic_diameters=False):
"""Plots a 2d figure of the tree's segments.
Args:
ax(matplotlib axes): on what to plot
tree(neurom.core.Tree or neurom.core.Neurite): plotted tree
plane(str): Any pair of 'xyz'
diameter_scale(float): Scale factor multiplied with segment diameters before plotting
linewidth(float): all segments are plotted with this width, but only if diameter_scale=None
color(str or None): Color of plotted values, None corresponds to default choice
alpha(float): Transparency of plotted values
realistic_diameters(bool): scale linewidths with axis data coordinates
Note:
If the tree contains one single point the plot will be empty
since no segments can be constructed.
"""
plane0, plane1 = _plane2col(plane)
section_segment_list = [(section, segment)
for section in iter_sections(tree)
for segment in iter_segments(section)]
colors = [_get_color(color, section.type) for section, _ in section_segment_list]
if realistic_diameters:
def _get_rectangle(x, y, linewidth):
"""Draw a rectangle to represent a secgment."""
x, y = np.array(x), np.array(y)
diff = y - x
angle = np.arctan2(diff[1], diff[0]) % (2 * np.pi)
return Rectangle(x - linewidth / 2. * np.array([-np.sin(angle), np.cos(angle)]),
np.linalg.norm(diff),
linewidth,
np.rad2deg(angle))
segs = [_get_rectangle((seg[0][plane0], seg[0][plane1]),
(seg[1][plane0], seg[1][plane1]),
2 * segment_radius(seg) * diameter_scale)
for _, seg in section_segment_list]
collection = PatchCollection(segs, alpha=alpha, facecolors=colors)
else:
segs = [((seg[0][plane0], seg[0][plane1]),
(seg[1][plane0], seg[1][plane1]))
for _, seg in section_segment_list]
linewidth = _get_linewidth(
tree,
diameter_scale=diameter_scale,
linewidth=linewidth,
)
collection = LineCollection(segs, colors=colors, linewidth=linewidth, alpha=alpha)
ax.add_collection(collection)
def _get_linewidth(tree, linewidth, diameter_scale):
'''calculate the desired linewidth based on tree contents
If diameter_scale exists, it is used to scale the diameter of each of the segments
in the tree
If diameter_scale is None, the linewidth is used.
'''
if diameter_scale is not None and tree:
linewidth = [2 * segment_radius(s) * diameter_scale
for s in iter_segments(tree)]
return linewidth
def test_segment_radius():
assert mm.segment_radius(((0, 0, 0, 4), (0, 0, 0, 6))) == 5
def test_segment_radius():
nt.ok_(mm.segment_radius(((0,0,0,4),(0,0,0,6))) == 5)
# p[COLS.R] yields the radius for point p.
# Note: this includes duplicated points at beginning of
# non-trunk sections
print('Mean radius of points:',
np.mean([s.points[:, COLS.R] for s in nm.iter_sections(nrn)]))
# get mean radius of neurite points in cell.
# p[COLS.R] yields the radius for point p.
# Note: this includes duplicated points at beginning of
# non-trunk sections
pts = [p[COLS.R] for s in nrn.sections[1:] for p in s.points]
print('Mean radius of points:', np.mean(pts))
# get mean radius of segments
print('Mean radius of segments:',
np.mean(list(mm.segment_radius(s) for s in nm.iter_segments(nrn))))
# get stats for the segment taper rate, for different types of neurite
for ttype in NEURITES:
ttt = ttype
seg_taper_rate = [
mm.segment_taper_rate(s)
for s in nm.iter_segments(nrn,
neurite_filter=tree_type_checker(ttt))
]
print('Segment taper rate (',
ttype,
'):\n mean=',
np.mean(seg_taper_rate),
', std=',